FIBO Operational Ontologies Briefing
for the Object Management Group
David Newman Strategic Planning Manager, Senior Vice President, Enterprise Architecture
Chair, Semantic Technology Program, EDM Council
March 20, 2013, Reston, VA
Wells Fargo Disclaimer
The content in this presentation represents only the views of the presenter and does not represent or imply acknowledged adoption by Wells Fargo Bank. Examples used within are purely hypothetical and are used for illustrative purposes only and are not intended to reflect Wells Fargo policy or intellectual property.
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2008 Global Financial Crisis Stimulated Need for Improved Financial Data Standards
• Financial industry needs better data standards: – to ensure there is high data consistency, promote data
comparability and facilitate transparency
– as a prerequisite for effective institutional and macroprudential risk analysis and reporting
• Financial data standards are needed for: – identification of legal entities, their jurisdictions and
control ownership hierarchies
– identification of financial contracts and instruments
– classification and data linkage for aggregation
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Principles for effective risk data aggregation and risk reporting Basel Committee on Banking Supervision, June 2012
“One of the most significant lessons learned from the global financial crisis that began in 2007 was that banks’ information technology (IT) and data architectures were inadequate to support the broad management of financial risks. Many banks lacked the ability to aggregate risk exposures and concentrations quickly and accurately at the bank group level, across business lines and between legal entities. “
Regulatory Requirements for Risk Data Aggregation and Reporting
• Per FSB and Basel, global SIFIs must comply with risk data aggregation requirements by early 2016. IT infrastructure and architecture need to support risk data aggregation capabilities in normal
and during stress times.
A bank should establish integrated data taxonomies and architecture across the banking group, which includes information on the characteristics of the data (metadata), as well as use of single identifiers and/or unified naming conventions for data including legal entities, counterparties, customers and accounts.
A bank should be able to capture and aggregate all material risk data across the banking group. Data should be available by business line, legal entity, asset type, industry, region and other groupings that permit identifying and reporting risk exposures, concentrations and emerging risks.
Risk reports should be accurate in times of stress and (largely) automated to minimize errors
Risk data must be complete and captured/aggregated across the enterprise
Risk data must be accurate and the firm must be able to reconcile/validate reports
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Some Operational Benefits of FIBO
Ensures that all instruments truly conform to the standard definition of the contract.
Displays meaning of contract to both humans and machines
Flags instruments that are exceptions or do not align
Data rollups and aggregations are based upon the reliability of knowing that the underlying data has been validated for conformity
Provides a rich flexible multi-tiered taxonomy
An instrument can be classified across multiple facets
Data can be rolled up and aggregated at multiple levels of the taxonomy
Data is linked in a network graph structure that allows for high data diversity and variability of relationships
Efficient querying and identification of complex relationships
Agile adding and linking of new and changing data relationships
Graph structures lend themselves well to visual representations of data
Metadata annotations are integrated into FIBO and provide standard definitions and useful links to related information
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Visualization of FIBO Derivatives Taxonomy (3 levels shown of multi-level taxonomy)
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FIBO has a Rich Multi-Tiered Taxonomy that can be used to Classify and Aggregate Data at Many Levels and Across Many Facets
* Screen shots from Gruff query and visualization tool from Franz Inc.
Legal Entity Ownership and Control
Relationships can be Queried and Displayed
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Semantic web enables data visualizations which are more holistic and descriptive than basic columnar views
FIBO aligns with LEI
Legend
FIBO Describes Business Entities, Legal
Jurisdictions and Country Locations
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Legend
Data relationships are shown as links within a network graph
Counterparty Trades, FIBO Classifications,
ISDA UPI Codes
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FIBO provides an automatic mapping between FIBO named product classes and identifiers and ISDA named product identifiers (or any other linked product taxonomies)
Credit Default Swap Transactions (top)
and Netting Transactions (bottom)
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FIBO can identify netting transactions (a CDS protection buyer becomes a CDS protection seller for the same reference entity but also creates a potential scenario for cascading risk)
Tabular view of credit default swap transactions
FIBO Identifies Instrument Contractual
Terms and Attributes
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Holistic view of a credit default swap, and the data types and classifications of many key attributes
FIBO Flags Instruments that Do Not Conform
to the Standard Contractual Definitions in FIBO
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Example: Reference Entity, a necessary CDS attribute, is missing from this particular CDS
Instrument is flagged by FIBO because it cannot be further classified as a specialization of a Credit Default Swap i.e. (Index CDS or Single Name CDS) indicating misalignment
Instruments that are conformant are fully classified at the most fine grained levels of the FIBO taxonomy and are deemed to be fully aligned with the contractual specification thus enhancing data reliability
Reference Entity is missing
FIBO Identifies Ultimate Parents, their
Descendents and Trading Counterparties
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This capability allows for the rollup of both positions and exposures of the subsidiaries to the level of the ultimate parent for risk analysis
FIBO Identifies International Trading Partners
via Country of Business Entity Legal Jurisdiction
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FIBO defines meaningful relationships between entities
Rollups of Total Positions by Country
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FIBO provides the ability to rollup positions for legal entities within jurisdictions that are domiciled within countries for any desired level of a product taxonomy
Business Entities and Aggregation by
FIBO Swap Contract Classes
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FIBO allows rollups of a business entity’s total position across trades for particular types of instruments and product classifications
Business Entities and Higher Level
Aggregation by FIBO Swap Contract Classes
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In this example, FIBO displays rollups for a business entity at the level of all derivatives contracts
Rollup of Ultimate Parents and their
Descendents Total Positions for Instruments
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FIBO supports the ability to rollup the total positions of an ultimate parent and it’s subsidiaries to show aggregate exposure for particular instruments
Rollup of Ultimate Parents and their Descendents
Total Swap Positions using FIBO Classes
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FIBO supports the ability to rollup the total positions of an ultimate parent and it’s subsidiaries to show aggregate exposure at increasingly higher levels of the taxonomy
Visualization of Transitive Exposures Across
Counterparties
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Visually demonstrates that Wall Street Bank has more direct and indirect exposures to counterparties than London Bank or other Banks.
Visualization of Transitive Exposures across
Counterparties with Higher Positions
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However when filtered by a threshold value for a higher total amount exposure, London Bank actually exhibits greater risk than Wall Street Bank, since it has a greater number of exposures that exceed the threshold value.
Visualization of trading relationships across counterparties provides a more intuitive and cognitively clear view of transitive exposures
Visualization of Ownership Hierarchies and
Exposures to Counterparties
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Legend
Solid blue lines represent ownership and control relations. Violet lines represent exposures due to trading
FIBO Metadata Annotations Provide Useful
Descriptions and Links to Related Information
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Annotations represents supplemental descriptive information that can be linked to the elements within FIBO and can be directly queried. Example of some annotations for ‘Interest Rate Swap Contract’ and related CFTC information. Provenance, policy, regulations, security constraints, etc. can also be linked.
Definitions for all types of ‘Swap Contracts’ are retrieved. Annotations eliminate need to define metadata using a separate independent product or repository.
Legacy Data can be Processed Semantically Without Requiring Conversion or Migration
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Semantic Information Integration
Platform
Legacy data can be collected from diverse sources, mapped, integrated with FIBO, classified (based upon alignment with business concepts) and then aggregated for query and reporting purposes
Risk Analyst
Ontologies
Semantic Triple Store
semantic query
Semantic vendors provide tools for data federation and integration
Excel to Ontology Mappings
Spread sheets
Legacy Relational Databases
Interest Rate Swap Map Interest Rate Swap Table
Credit Default Swap Table
Credit Default Swap Map
Excel Adapter
SQL to Ontology Mappings
Relational Adapter Relational
Adapters
FIBO Swap Contract Ontology
Excel interface
Ontology to FIBO Mappings
Integrated data
Mapping between legacy data and ontologies is the only prerequisite
Proposed FIBO Architecture for Institutional and Macroprudential Oversight
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Semantic Information Integration
Platform
Trusted Financial Linked
Data Cloud
Regulatory Risk Analyst
Semantic Network Graph Analysis
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Trading System
Financial Institutions
Swap Trade & Compliance Reporting
FpML
Semantic Information Integration
Platform
Regulatory Agencies
Trading & Compliance System(s)
Semantic Triple Store
Institutional Risk Analyst
Legacy Database(s)
Semantically defined financial data standards ensures data quality and fidelity between institutions and regulators
Semantic Triple Store
Informational Sharing across Regulatory Agencies
Mapping Mapping
Swap Data Repository Database(s)
Legal Entity Data Provider(s)
Legacy Database(s)
Mapping
Mapping Mapping
Ontologies
Ontologies